
TLO
Unlocking insights from unstructured data.

Accelerate research with AI that automates literature reviews and data extraction from academic papers.

Elicit is a sophisticated AI research engine designed to automate the systematic review process for scientists and researchers. Unlike general-purpose LLMs, Elicit utilizes a Retrieval-Augmented Generation (RAG) architecture specialized for a corpus of over 200 million academic papers sourced primarily from Semantic Scholar. Its technical core involves decomposing complex research queries into sub-tasks, performing semantic vector searches to retrieve high-relevance snippets, and then utilizing fine-tuned models to synthesize findings with direct citations. By 2026, Elicit has established itself as the industry standard for 'verifiable AI,' moving beyond simple chat interfaces to provide structured data extraction where users can define custom columns (e.g., sample size, methodology, p-values) which the AI populates by reading across hundreds of PDFs simultaneously. The platform's commitment to transparency is reflected in its provenance tracking, showing exactly which sentence in a paper justifies a generated summary, thereby mitigating the hallucination risks inherent in standard generative models.
Elicit is a sophisticated AI research engine designed to automate the systematic review process for scientists and researchers.
Explore all tools that specialize in literature review. This domain focus ensures Elicit delivers optimized results for this specific requirement.
Uses LLMs to parse unstructured PDF text into structured data based on user-defined prompts (e.g., 'What was the dosage?').
Vector-based retrieval that finds papers based on meaning rather than exact keyword matches.
Generates a highly compressed summary of a paper's main contribution using specialized summarization models.
Automatically categorizes papers into Systematic Reviews, RCTs, Longitudinal Studies, etc.
A UI layer that highlights the exact source text in the original PDF that corresponds to every AI-generated claim.
Visualizes the relationship between papers based on citation networks and semantic similarity.
Translates queries into multiple languages to search international academic databases.
Create an account via email or SSO.
Enter a research question in the 'Search' interface to query the 200M+ paper database.
Filter results by date, study type (e.g., RCT), and citation count.
Select relevant papers to add to a personalized 'Workspace'.
Define 'Extraction Columns' such as population, intervention, or outcome.
Upload custom PDF files for papers not indexed in the public database.
Run the extraction engine to populate the data matrix.
Verify AI-generated summaries by clicking the linked citations to view source context.
Export the structured table to CSV or JSON for statistical analysis.
Use the 'Synthesis' feature to generate a high-level overview of the findings across all selected papers.
All Set
Ready to go
Verified feedback from other users.
"Highly praised for its ability to save time during literature reviews, though some users find the credit system expensive."
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